Lymphatic vessel segmentation in optical coherence tomography by adding U-Net-based CNN for artifact minimization

Pei-Yu Lai; Chung-Hsing Chang; Hong-Ren Su; Wen-Chuan Kuo

doi:10.1364/BOE.389373

Lymphatic vessel segmentation in optical coherence tomography by adding U-Net-based CNN for artifact minimization

Biomed Opt Express. 2020 Apr 23;11(5):2679-2693. doi: 10.1364/BOE.389373. eCollection 2020 May 1.

Authors

Pei-Yu Lai¹, Chung-Hsing Chang^{2

3}, Hong-Ren Su⁴, Wen-Chuan Kuo¹

Affiliations

¹ Department of Biophotonics, National Yang-Ming University, 155, Sec-2, Li-Nong Street, Taipei 112, Taiwan.
² Skin Institute, Hualien Tzu Chi Hospital, Hualien, Taiwan.
³ Institute of Medical Sciences, Tzu Chi University, Hualien, Taiwan.
⁴ Super Genius AItek Co., Ltd, New Taipei City, Taiwan.

Abstract

The lymphatic system branches throughout the body to transport bodily fluid and plays a key immune-response role. Optical coherence tomography (OCT) is an emerging technique for the noninvasive and label-free imaging of lymphatic capillaries utilizing low scattering features of the lymph fluid. Here, the proposed lymphatic segmentation method combines U-Net-based CNN, a Hessian vesselness filter, and a modified intensity-thresholding to search the nearby pixels based on the binarized Hessian mask. Compared to previous approaches, the method can extract shapes more precisely, and the segmented result contains minimal artifacts, achieves the dice coefficient of 0.83, precision of 0.859, and recall of 0.803.